Enhanced Intelligent Character Recognition (ICR) Approach Using Diagonal Feature Extraction and Euler Number as Classifier with Modified One-Pixel Width Character Segmentation Algorithm

Abstract

In this technological age, handwriting communication is still an essential aspect in the lives of people and relating to each other. This study was created to identify the most suitable set of algorithms that can be used and determine how effective it would be in recognizing cursive handwritten texts. The proponents created a system that accepts a handwritten text image as input, undergoes processing stages and outputs a text based on the features extracted per character using the Diagonal Feature Extraction, and classification using Euler Number with the use of the Modified One-Pixel Width Character Segmentation Algorithm. A total of 100 handwritten text images are used in evaluating the system. The system achieved a character recognition rate of 88.7838% and word recognition rate of 50.4348%.